scholarly journals Image Recognition of Male Oilseed Rape (Brassica napus) Plants Based on Convolutional Neural Network for UAAS Navigation Applications on Supplementary Pollination and Aerial Spraying

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 62
Author(s):  
Zhu Sun ◽  
Xiangyu Guo ◽  
Yang Xu ◽  
Songchao Zhang ◽  
Xiaohui Cheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Brassica Napus ◽  
Convolutional Neural Network ◽  
Seed Production ◽  
Image Recognition ◽  
Oilseed Rape ◽  
Center Line ◽  
Recognition Model ◽  
Aerial Spraying ◽  
Mean Square Errors

To ensure the hybrid oilseed rape (OSR, Brassica napus) seed production, two important things are necessary, the stamen sterility on the female OSR plants and the effective pollen spread onto the pistil from the OSR male plants to the OSR female plants. The unmanned agricultural aerial system (UAAS) has developed rapidly in China. It has been used on supplementary pollination and aerial spraying during the hybrid OSR seed production. This study developed a new method to rapidly recognize the male OSR plants and extract the row center line for supporting the UAAS navigation. A male OSR plant recognition model was constructed based on the convolutional neural network (CNN). The sequence images of male OSR plants were extracted, the feature regions and points were obtained from the images through morphological and boundary process methods and horizontal segmentation, respectively. The male OSR plant image recognition accuracies of different CNN structures and segmentation sizes were discussed. The male OSR plant row center lines were fitted using the least-squares method (LSM) and Hough transform. The results showed that the segmentation algorithm could segment the male OSR plants from the complex background. The highest average recognition accuracy was 93.54%, and the minimum loss function value was 0.2059 with three convolutional layers, one fully connected layer, and a segmentation size of 40 pix × 40 pix. The LSM is better for center line fitting. The average recognition model accuracies of original input images were 98% and 94%, and the average root mean square errors (RMSE) of angle were 3.22° and 1.36° under cloudy day and sunny day lighting conditions, respectively. The results demonstrate the potential of using digital imaging technology to recognize the male OSR plant row for UAAS visual navigation on the applications of hybrid OSR supplementary pollination and aerial spraying, which would be a meaningful supplement in precision agriculture.

scholarly journals A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 81
Author(s):  
Jianbin Xiong ◽  
Dezheng Yu ◽  
Shuangyin Liu ◽  
Lei Shu ◽  
Xiaochan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Plant Species ◽  
Image Recognition ◽  
Recurrent Neural Network ◽  
Plant Diseases ◽  
Learning Methods ◽  
Smart Agriculture ◽  
Important Branch

Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed.

scholarly journals Detection of Strawberry Diseases Using a Convolutional Neural Network

Plants ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 31
Author(s):  
Jia-Rong Xiao ◽  
Pei-Che Chung ◽  
Hung-Yi Wu ◽  
Quoc-Hung Phan ◽  
Jer-Liang Andrew Yeh ◽  
...  
Keyword(s):  
Neural Network ◽  
Powdery Mildew ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Cost Effective ◽  
Gray Mold ◽  
Leaf Blight ◽  
Crown Rot ◽  
Accuracy Rate ◽  
Fruit Disease

The strawberry (Fragaria × ananassa Duch.) is a high-value crop with an annual cultivated area of ~500 ha in Taiwan. Over 90% of strawberry cultivation is in Miaoli County. Unfortunately, various diseases significantly decrease strawberry production. The leaf and fruit disease became an epidemic in 1986. From 2010 to 2016, anthracnose crown rot caused the loss of 30–40% of seedlings and ~20% of plants after transplanting. The automation of agriculture and image recognition techniques are indispensable for detecting strawberry diseases. We developed an image recognition technique for the detection of strawberry diseases using a convolutional neural network (CNN) model. CNN is a powerful deep learning approach that has been used to enhance image recognition. In the proposed technique, two different datasets containing the original and feature images are used for detecting the following strawberry diseases—leaf blight, gray mold, and powdery mildew. Specifically, leaf blight may affect the crown, leaf, and fruit and show different symptoms. By using the ResNet50 model with a training period of 20 epochs for 1306 feature images, the proposed CNN model achieves a classification accuracy rate of 100% for leaf blight cases affecting the crown, leaf, and fruit; 98% for gray mold cases, and 98% for powdery mildew cases. In 20 epochs, the accuracy rate of 99.60% obtained from the feature image dataset was higher than that of 1.53% obtained from the original one. This proposed model provides a simple, reliable, and cost-effective technique for detecting strawberry diseases.

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